The aim of this application is the development and evaluation of improved statistical methods for the design, conduct and analysis of a range of medical and biomedical follow-up studies. There are three projects and one core unit. Project 1 is concerned with statistical methods for disease prevention and risk factor intervention trials. The development and evaluation of flexible and comprehensive methods for the analysis of multivariate failure time data, as arises in trials with multiple disease outcomes and in group randomized trials, is a particular emphasis area. Methods for assessing overall benefits versus risks in prevention trial monitoring and reporting is a new emphasis area, while methods for community intervention trials is a continuing emphasis area. There will also be emphasis on the methods for extracting additional information from prevention trials, including methods for identification and use of surrogate and auxiliary endpoints, for explanatory analyses, and for extending trial results by using related observational data. Project 2 is concerned with statistical methods for epidemiologic studies. There will be an enhanced emphasis on genetic epidemiologic methods, including the development of a multistage design for family studies and of related estimating equation- and frailty-based analysis procedures. There will also be continuing emphases on methods for accomodating covariate measurement errors in cohort and case-control data analysis, and/or methods for the design and analysis of aggregate data studies of disease rates and risk factor survey data, as well as a new emphasis on nonparametric methods for relative risk estimation. Project 3 is concerned with statistical methods for the efficient design and analysis of clinical studies. A particular emphasis will include strategies for multi-arm trials, including factorial designs and ordered alternative designs, and on monitoring issues in the design and analysis of Phase III (comparative) trials. Other emphases will include the development of exploratory methods in survival analysis, multistate and multivariate survival analysis methods, and other topics, such as study of the small sample properties of the logrank test and group sequential strategies for testing biological specimens, that are pertinent to the design or analysis of clinical studies. A small administrative and computing core will serve to coordinate, standardize and facilitate the methodologic work. The proposed developments have the potential to increase the efficiency and reliability, and to decrease the cost of various important types of medical/biomedical studies, and to enhance the use of routinely collected biomedical data.